Scholar4AI Updated 2026-11-03 14:43 UTC+09:00

Lightning-AI/pytorch-lightning: papers linking to it

GitHub · 31,439 stars · 3,843 forks · Python · license Apache-2.0 · created 2019 · github.com/Lightning-AI/pytorch-lightning

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Research papers in the 480 tracked journals that link to Lightning-AI/pytorch-lightning (https://github.com/Lightning-AI/pytorch-lightning), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

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Key findings#

  • 13 papers in the tracked journals link to the GitHub repository Lightning-AI/pytorch-lightning in their Crossref records, the first in 2022; most of them (6) in 2025.
  • By category: AI (13); the journals with the most are Nature Machine Intelligence (4), Applied Intelligence (3) and Neural Networks (2).
  • Linked repositories derived from it: 1, such as PyTorchLightning/pytorch-lightning: 0.7.6 release.

By year#

2022: 1 paper2023: 4 papers2024: 1 paper2025: 6 papers2026: 1 paper
20222026
Papers linking to Lightning-AI/pytorch-lightning by publication year
YearPapers
20221
20234
20241
20256
20261

Journals#

Journals whose papers link to Lightning-AI/pytorch-lightning most
JournalPapers
Nature Machine Intelligence4
Applied Intelligence3
Neural Networks2
International Journal of Computer Vision1
Knowledge and Information Systems1
Linked repositories Lightning-AI/pytorch-lightning derives from or that derive from it
RepositoryRelationPapers
PyTorchLightning/pytorch-lightning: 0.7.6 releaseZenodo archive of Lightning-AI/pytorch-lightning7
Papers linking to Lightning-AI/pytorch-lightning, most cited first
PublishedTitleJournalAuthorsCitationsLink
2023-10-23Neural scaling of deep chemical modelsNature Machine IntelligenceNathan C. Frey, Ryan Soklaski, Simon Axelrod et al.125cites it
2023-11-06Development of the Senseiver for efficient field reconstruction from sparse observationsNature Machine IntelligenceJavier E. Santos, Zachary Fox, Arvind T. Mohan et al.101cites it
2022-10-03Physically constrained generative adversarial networks for improving precipitation fields from Earth system modelsNature Machine IntelligencePhilipp Hess, Markus Drüke, Stefan Petri et al.93cites it
2025-05-23Advancing molecular machine learning representations with stereoelectronics-infused molecular graphsNature Machine IntelligenceDaniil A. Boiko, Thiago Reschützegger, Benjamín Sánchez-Lengeling et al.25cites it
2024-08-20Performance metrics for multi-step forecasting measuring win-loss, seasonal variance and forecast stability: an empirical studyApplied IntelligenceEivind Strøm, Odd Erik Gundersen20cites it
2023-06-21Auto CNN classifier based on knowledge transferred from self-supervised modelApplied IntelligenceJaydeep Kishore, Snehasis Mukherjee8cites it
2023-12-29SyDog-Video: A Synthetic Dog Video Dataset for Temporal Pose EstimationInternational Journal of Computer VisionMoira Shooter, Charles Malleson, Adrian Hilton7cites it
2025-11-25Interpretable Deep Reinforcement Learning Via Concept-Based Policy DistillationMachine LearningYanzhe Bekkemoen, Helge Langseth A5cites it
2025-04-16Deep learning solutions for audio event detection in a swine barn using environmental audio and weak labelsApplied IntelligenceAndré Moreira Souza, Lori Kobayashi, Lucas Tassoni Andrietta et al.3cites it
2025-07-21CoSTI: Consistency models for (a faster) spatio-temporal imputationKnowledge-Based SystemsJavier Solí­s-Garcí­a, Belén Vega-Márquez, Juan A. Nepomuceno et al.2cites it
2025-08-23Raw event-based adversarial attacks for Spiking Neural Networks with configurable latenciesNeural NetworksXiao Du, Wanli Shi, Xiaohan Zhao et al.0cites it
2025-08-28Focal Sampling: SGD biased towards early important samples for efficient image classification with augmentation selectionKnowledge and Information SystemsAlessio Quercia, Fernanda Nader, Abigail Morrison et al.0cites it
2026-06-03Symplectic convolutional neural networksNeural NetworksSüleyman Yıldız, Konrad Janik, Peter Benner0cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). Lightning-AI/pytorch-lightning: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-lightning-ai-pytorch-lightning

@misc{scholar4ai-code-github-lightning-ai-pytorch-lightning,
  author = {{Scholar4AI}},
  title = {Lightning-AI/pytorch-lightning: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-lightning-ai-pytorch-lightning}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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